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The home advantage and COVID-19: the crowd support effect on the english football premier league and the championship

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  • Johan Lyhagen

    (Uppsala University)

Abstract

It is well known that there is an home advantage in football (American English: soccer) where the home team wins in about 45% of the games compared to the 27% of the away team. This has mainly been attributed to the support of the home audience and is commonly denoted the crowd support effect. The COVID-19 pandemic forced many football leagues to play the games without spectators thus making it possible to analyse the effect of crowd support in football. We analyse more than 18,000 games in the two top English football leagues during the period 2001–2020 for the Premier league and the Championship with an ordinal logistic model with explanatory variables (e.g., previous team performance, a time trend, league dummy) including a pandemic dummy. We discovered that the absence of spectators has no impact on the outcome probability in the Premier League. However, it significantly reduces the probability of the home team wins in the Championship.

Suggested Citation

  • Johan Lyhagen, 2025. "The home advantage and COVID-19: the crowd support effect on the english football premier league and the championship," Computational Statistics, Springer, vol. 40(4), pages 1919-1932, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-025-01600-x
    DOI: 10.1007/s00180-025-01600-x
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    References listed on IDEAS

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    1. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    2. Kai Fischer & Justus Haucap, 2021. "Does Crowd Support Drive the Home Advantage in Professional Football? Evidence from German Ghost Games during the COVID-19 Pandemic," Journal of Sports Economics, , vol. 22(8), pages 982-1008, December.
    3. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    4. Bäker Agnes & Mechtel Mario & Vetter Karin, 2012. "Beating thy Neighbor: Derby Effects in German Professional Soccer," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(3), pages 224-246, June.
    5. Leitner, Christoph & Zeileis, Achim & Hornik, Kurt, 2010. "Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008," International Journal of Forecasting, Elsevier, vol. 26(3), pages 471-481, July.
    6. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    7. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
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